Deep|LLM: GPT-6 Astra Opens New Markets Beyond Coding; Scaling Laws Keep Driving Compute Demand Knowledge work inflection: GPT-6 Astra is OpenAI’s first major version change since GPT-5, trained on more than 100,000 GPUs and released on September 3 across paid ChatGPT, API, Azure and Bedrock. We view Astra as the Claude 3.7 moment for knowledge work: computer use is now a core capability, opening a market far larger than coding, with white-collar wages alone exceeding $10 trillion. Computer use expands the TAM: Astra’s OSWorld score rose to 72.6%, while average task time fell from 75 minutes to 40. On Agents’ Last Exam, it scores 59.3% versus 55.5% for Opus 5, using 65% fewer output tokens. Early traction is likely in structured, verifiable workflows such as Power BI, Excel, financial modeling, tax forms, legal documents, CAD and Blender. RSI and AI for science: Astra is the first flagship where an earlier model played a substantial role in training, supporting the recursive self-improvement thesis. OpenAI reports that by mid-August, each human workday corresponded to 3.1 agent workdays, while top-decile researchers consumed $7,000 of inference tokens per person per day. Astra also solved 2 of 68 open Erdős problems and reached 64.6% on Terminal-Bench Science versus 52.6% for Fable 5.1. Compute demand remains strong: Industry conversations suggest GPT-6 used roughly 10x the training compute of GPT-5, with much of the increase going into post-training, RL, experiments and synthetic data rather than parameter count. Vera Rubin, Rubin Ultra Kyber NVL576, TPU 8t and large TPU/GPU deployments in 2027 could support another order-of-magnitude scaling cycle, benefiting large-scale interconnect and optics. Detailed Report